METHOD FOR DECISION MAKING USING ARTIFICIAL INTELLIGENCE

• US 20110055065A1
• Filed: 08/06/2010
• Published: 03/03/2011
• Est. Priority Date: 08/10/2009
• Status: Active Grant

• Current Assignee: Mory Brenner
• Original Assignee: Mory Brenner
• Inventors: Brenner, Mory
• Granted Patent: US 8,494,936 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

A method of decision making using artificial intelligence that: receives data associated with a user regarding the situation of the user; identifies action options that the user might pursue; computes normalized scores for each of the action options based on the eligibility and likely outcome of the user pursuing the respective action option; compares the scores to a minimum threshold and to each other using quantitative and qualitative metrics; outputs a list of action options to the user as primary and secondary options to pursue based on the comparison; receives a selection from the user based on the list of action options; and transmits a message to a party who provides the action option(s) based on the selection. The method is, for example, applicable to decision making in the context of personal finance and debt assistance.

Claims

1. A method for artificial intelligence decision making using a network entity of a network, the method comprising:

computing, using the network entity, scores associated with at least two action options based on data associated with a user;

comparing, using the network entity, the scores to a minimum threshold;

if each of the scores is less than the minimum threshold, outputting, using the network entity, an alert including an identification of one or more of the at least two action options having a highest score;

or, else,

if at least one of the scores of the at least two action options is greater than or equal to the minimum threshold, comparing, using the network entity, the scores to each other;

categorizing, using the network entity, the at least two action options as a primary option, a secondary option or neither based on the comparisons of the scores; and

outputting, using the network entity, a sorted list of the at least two action options, wherein the sorted list is selected from the group consisting of;

the primary option and the secondary option.

2. The method of claim 1, wherein the decision making relates to personal finance and debt assistance of a user.

3. The method of claim 1, further comprising:

receiving, using the network entity, the data associated with a user, wherein the data includes information selected from the group consisting of;

an unique identifier(s), an address, an e-mail address, a telephone number, financial information associated with an income, a fixed income, an asset, a net worth, a tax exempt asset, an expense, a loan, a rental agreement, a mortgage and a credit card including payment obligations associated with the same.

4. The method of claim 1, further comprising:

generating, using the network entity, a targeted information form based on the data associated with a user that was previously entered; and

transmitting, to a user terminal and using the network entity, the targeted information forms; and

receiving, from the user terminal and using the network entity, the information associated with the user in response to the targeted information form;

wherein the targeted information form inquires about a minimum amount of information that is required to make an informed decision.

5. The method of claim 1, further comprising:

receiving, from a user terminal of a user and using the network entity, the data associated with a user; and

verifying, using the network entity, one or more elements of the data associated with the user by calculating the one or more elements based on other elements of the data associated with the user.

6. The method of claim 1, further comprising:

gathering, from a third party server or database and using the network entity, additional data based on the data received from the user terminal; and

verifying, using the network entity, the data associated with the user by comparing the data received from the user terminal to the additional data gathered from the third party server or database.

7. The method of claim 1, further comprising:

scanning, using the network entity, the data associated with the user for an early indicator; and

, if the early indicator is identified by the scanning,

alerting, using the network entity, a user regarding the early indicator.

8. The method of claim 1, further comprising:

identifying, using the network entity, the at least two action options based on the data associated with a user.

9. The method of claim 1, wherein:

the at least two action options are selected from the group consisting of;

a do nothing option;

a balance transfer to zero percent (0%) annual percentage return (APR) credit card option;

an unsecured to secured debt option;

an unsecured personal loan option;

a non-profit credit counseling option;

a chapter 7 bankruptcy option;

a debt settlement with a reduced balance due option;

a fixed income option; and

a chapter 13 bankruptcy option.

10. The method of claim 1, wherein:

the scores indicate an eligibility of the user to pursue the respective action option and a likely outcome of the user pursuing the respective action option; and

the minimum threshold indicates a minimum sufficiency of the user pursuing one of the at least two action options in terms of both an eligibility of the user to pursue the respective action option and a likely outcome of the user pursuing the respective action option.

11. The method of claim 1, wherein:

the scores are computed using an algorithm specific to each respective action option;

each of the scores falls within a defined score range of all possible scores; and

the scores computed using algorithms specific to the at least two action options fall within independent sub-ranges of the defined score range.

12. The method of claim 1, wherein:

the scores are computed using an algorithm specific to each respective action option; and

the algorithm is based on factors selected from the group consisting of;

legally defined formulae, accounting standards, finance standards, industry knowledge and expertise, and statistical data associated with a peer group of a user.

13. The method of claim 1, wherein:

the step of categorizing further comprises;

categorizing, using the network entity, each of the at least two action options according to a quantitative metric and a qualitative metric.

14. The method of claim 1, wherein:

the step of comparing further comprises;

determining, using the network entity, a highest score of the scores of the at least two action options.

15. The method of claim 14, wherein:

the step of categorizing further comprises;

categorizing, using the network entity, one of the at least two action options as the primary option when the score of the one of the at least two action options is greater than the highest score minus a similarity threshold and greater than the similarity threshold;

or, else,

categorizing, using the network entity, one of the at least two action options as the secondary option when the score of the one of the at least two action options is less than the highest score minus a similarity threshold and greater than the minimum threshold.

16. The method of claim 14, wherein:

the step of categorizing further comprises;

determining, using the network entity, a variable similarity threshold by comparing the highest score to a defined score range of all possible score values; and

categorizing, using the network entity, one of the at least two action options to the list of primary options when the score of the one of the at least two action options is greater than the highest score minus the variable similarity threshold and greater than the minimum threshold.

17. The method of claim 1, wherein:

the step of categorizing further comprises;

categorizing, using the network entity, at least one of the at least two action options as the primary option.

18. The method of claim 1, wherein:

the at least two action options comprise at least three action options;

the step of categorizing further comprises;

categorizing, using the network entity, at least one of the at least three action options as the secondary option; and

at least one of the at least three action options is not categorized as either the primary option or the secondary option.

19. The method of claim 1, wherein:

the sorted list of action options is outputted to a user terminal of a user; and

the method further comprising;

receiving, from the user terminal of a user and using the network entity, a selection from the sorted list of action options; and

acting, using the network entity, based on the selection from the sorted list of action options.

20. The method of claim 19, wherein:

the sorted list of action options includes data comprising;

a name or title associated with each action option of the sorted list of action options, and indicia that at least one of the at least two action options is categorized as the primary option.

21. The method of claim 1, further comprising:

identifying, using the network entity, at least one party who provides one of the sorted list of action options based on the data associated with the user;

transmitting, to a user terminal of a user and using the network entity, information associated with the at least one party; and

receiving, from the user terminal of a user and using the network entity, a selection of one or more of the at least one party from the information associated with the at least one party.

22. The method of claim 21, further comprising:

determining, using the network entity, if an initial alert is set by the one or more of the at least one party; and

, if so,

transmitting, to the user terminal of a user and using the network entity, the initial alert set by the one or more of the at least one party based on the selection of the one or more of the at least one party; and

receiving, from the user terminal of a user and using the network entity, a response to the initial alert; and

transmitting, using the network entity, a message to the one or more of the at least one party based on the selection of the one or more of the at least one party, the message including information selected from the group consisting of;

a greeting, a follow-up instruction, the data associated with a user, and the sorted list of action options.

23. The method of claim 1, wherein:

the step of outputting further comprises;

transmitting, using the network entity, a message to a party who provides one of the sorted list of action options, the message including data selected from the group consisting of;

a greeting, a follow-up instruction, the data associated with the user, and the sorted list of action options; and

transmitting, to a user terminal of a user and using the network entity, a confirmation, the confirmation including data selected from the group consisting of;

the greeting, the follow-up instruction, the data associated with the user, and the sorted list of action options.

24. A method for artificial intelligence decision making using a network entity of a network, the method comprising:

comparing, using the network entity, scores associated with at least two action options to a minimum threshold and to each other, wherein the scores are based on data associated with a user and algorithms specific to each of the at least two action options;

generating, using the network entity, a sorted list of action options based on the comparison of the at least two action options;

when each of the at least two action options are less than the minimum threshold, outputting, to a user terminal of a user and using the network entity, an alert that none of the at least two action options are at least minimally sufficient; and

outputting, to the user terminal of a user and using the network entity, the sorted list of action options.

25. A method for artificial intelligence decision making using a network entity of a network, the method comprising:

categorizing, using the network entity, at least two action options based on scores associated with the respective action option, wherein the scores are based on data associated with a user and algorithms specific to each of the at least two action options, wherein the step of categorizing further comprises;

determining, using the network entity, a highest score based on the scores;

determining, using the network entity, a variable similarity threshold by comparing the highest score to a defined score range of all possible score values; and

categorizing, using the network entity, one or more of the at least two action options as a primary option when the score associated with the respective action option is greater than a minimum threshold and either greater than the highest score minus a similarity threshold or greater than the highest score minus the variable similarity threshold;

or, else,

categorizing, using the network entity, one or more of the other of the at least two action options as a secondary option when the score associated with the respective action option is greater than the minimum threshold;

transmitting, to a user terminal of a user and using the network entity, a sorted list of the at least two action options based on the categorization of the at least two action options;

receiving, from the user terminal of a user and using the network entity, a selection of one or more of the at least two action options from the sorted list; and

transmitting, using the network entity, a message to a party who provides the one or more of the at least two action options based on the selection of the one or more of the at least two action options.